How to use fuzzy screening system and data envelopment analysis for clustering sustainable suppliers? A case study in Iran

Mohammad Izadikhah, Reza Farzipoor Saen*, Kourosh Ahmadi, Mohadeseh Shamsi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Purpose: The aim of this paper is to classify suppliers into some clusters based on sustainability factors. However, there might be some unqualified suppliers and we should identify and remove those suppliers before clustering. Design/methodology/approach: First, using fuzzy screening system, the authors identify and remove the unqualified suppliers. Then, the authors run their proposed clustering method. This paper proposes a data envelopment analysis (DEA) algorithm to cluster suppliers. Findings: This paper presents a two-aspect DEA-based algorithm for clustering suppliers into clusters. The first aspect applied DEA to consider efficient frontiers and the second aspect applied DEA to consider inefficient frontiers. The authors examine their proposed clustering approach by a numerical example. The results confirmed that their method can cluster DMUs into clusters. Originality/value: The main contributions of this paper are as follows: This paper develops a new clustering algorithm based on DEA models. This paper presents a new DEA model in inefficiency aspect. For the first time, the authors’ proposed algorithm uses fuzzy screening system and DEA to select suppliers. Our proposed method clusters suppliers of MPASR based on sustainability factors.

Original languageEnglish
Pages (from-to)199-229
Number of pages31
JournalJournal of Enterprise Information Management
Volume34
Issue number1
DOIs
Publication statusPublished - Jun 16 2020

Keywords

  • Data envelopment analysis (DEA)
  • DEA-Based clustering method
  • Enhanced Russell model (ERM)
  • Fuzzy screening system
  • Sustainable supply chain management

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Information Systems
  • Management of Technology and Innovation

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